{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Taming time series\n",
    "\n",
    "Time series are collections of points collected at successive times, usually with equal time intervals between them.\n",
    "\n",
    "## Importing and visualising Time Series data\n",
    "\n",
    "In this module, we will consider the \"bikes\" dataset again but this time taking a time-series perspective. \n",
    "\n",
    "* Load the data `bikes.csv`\n",
    "* Specify that you want to parse the column `'dates'` as dates using `parse_dates=['dates']` and\n",
    "* Use the dates as the index column\n",
    "\n",
    "Check with `.head()` that everything is fine then\n",
    "* Display the temperature time series (you can either use `pandas` plotting facility by doing `bikes.plot(...)` or use `matplotlib` directly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>2.716070</td>\n",
       "      <td>45.715346</td>\n",
       "      <td>21.414957</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>2.896673</td>\n",
       "      <td>54.267219</td>\n",
       "      <td>15.136882</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>4.235654</td>\n",
       "      <td>45.697702</td>\n",
       "      <td>17.034578</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>3.112643</td>\n",
       "      <td>50.237349</td>\n",
       "      <td>10.091568</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>2.723918</td>\n",
       "      <td>49.144928</td>\n",
       "      <td>15.738204</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature   humidity  windspeed  count\n",
       "date                                                \n",
       "2011-01-03     2.716070  45.715346  21.414957  120.0\n",
       "2011-01-04     2.896673  54.267219  15.136882  108.0\n",
       "2011-01-05     4.235654  45.697702  17.034578   82.0\n",
       "2011-01-06     3.112643  50.237349  10.091568   88.0\n",
       "2011-01-07     2.723918  49.144928  15.738204  148.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = pd.read_csv('data/bikes.csv', parse_dates=['date'], index_col='date')\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c8c1f98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: only plot the temperature\n",
    "With the graph above it's hard to get an indication for the temperature in the data. Plot a graph with just the temperature as y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ae7a390>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes['temperature'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Number of bikes in a given month\n",
    "\n",
    "From the chart above, you can see that the data covers two years and that there is a similar pattern in both years which is to be expected for the temperatures. \n",
    "\n",
    "The data can be queried according to dates. For example, you can aggregate the data from just January 2012 and check the sum of bikes hired that month. Can you compare that with the number of bikes hired in August? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_start = pd.Timestamp(\"1st January 2012\")\n",
    "jan_end = pd.Timestamp(\"31st January 2012\")\n",
    "bikes_jan = bikes[jan_start:jan_end]['count'].sum()\n",
    "\n",
    "\n",
    "print(\"{0:.0f} bikes in January vs {1:.0f} bikes in August.\".format(bikes_jan, bikes_aug))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can aggregate time series by resampling the points on a coarser time level. \n",
    "\n",
    "* Use the `.resample` to get the data corresponding to monthly averages\n",
    "* Display the `temperature` time series for the monthly averages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes_monthly = bikes.resample('M').mean()\n",
    "\n",
    "plt.figure(figsize=(12, 3))\n",
    "plt.plot(bikes_monthly.temperature, \"-o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resample by the mean of each week and uses the humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes.plot(y='humidity', figsize=(12, 3), fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 3))\n",
    "plt.plot(bikes_weeks.humidity, \"-o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing custom date formats\n",
    "\n",
    "When you loaded the bikes dataset, Pandas automatically detected the format of the dates for you.\n",
    "This might often \"just work\" but there often will be cases where you need to be careful about parsing and might have to do it yourself.\n",
    "\n",
    "Load the data `NZAlcoholConsumption` and have a look at it without specifying a column to parse for dates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alcohol_consumption = pd.read_csv('data/NZAlcoholConsumption.csv')\n",
    "alcohol_consumption.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains data aggregated by quarters, the timestamp is formatted in a string where the first 4 characters represent the year and the last two the quarter. \n",
    "To transform the timestamps in dates that pandas can directly use, you can write a parser function. \n",
    "\n",
    "\n",
    "### Exercise: parsing quarter\n",
    "Write a function `parse_quarter` that takes a string of the form `YYYYQN` and convert it to `pandas.Timestamp` object. Use the following conversion for the quarters:\n",
    "\n",
    "* Q1 --> mar 31\n",
    "* Q2 --> jun 30\n",
    "* Q3 --> sep 20\n",
    "* Q4 --> dec 31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Check that it works!\n",
    "print(parse_quarter(\"2000Q3\")) # should show 2000-09-20 00:00:00"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Giving the parser to pandas\n",
    "\n",
    "Pandas can parse dates using a custom made parser such as the one you just defined. For this just specify your function in the `date_parser` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload the data using your parser, set the index to the date \n",
    "alcohol_consumption = pd.read_csv('data/NZAlcoholConsumption.csv', \n",
    "                                  parse_dates=['DATE'], \n",
    "                                  date_parser=parse_quarter,\n",
    "                                  index_col='DATE')\n",
    "alcohol_consumption.sort_index(inplace=True)\n",
    "alcohol_consumption.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Display the time series\n",
    "\n",
    "Now, have a look at the consumtion of wine and beer, show both on the same figure. Discuss the two time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots show that the two time series have similar patterns in terms of seasonality but different trends.\n",
    "Both show that alcohol consumption is maximum in the last quarter of the year and is usually at its lowest in the second quarter. \n",
    "The average beer consumption seems stable during the years, while the wine consumption seems to be steadily increasing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: resample the data per year (12 months) \n",
    "Can you resample the data per year (12 months) and see whether the trends come out better? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 3))\n",
    "plt.plot(alc_yearly.TotalWine, \"-o\", label=\"Wine\")\n",
    "plt.plot(alc_yearly.TotalBeer, \"-o\", label=\"Beer\")\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Windows\n",
    "\n",
    "In the cells below you will explore the effect of applying a \"Rolling Average\" to the data i.e.: look at a number of successive points, take the average, and replace the window by the average (either at the extreme right of the window, or at the center)\n",
    "\n",
    "* Use the `rolling` method from `pd.Series` ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.rolling.html#pandas.Series.rolling))\n",
    "* specify a window of 4 points\n",
    "\n",
    "plot the averaged line and the original time series and discuss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(alcohol_consumption.TotalWine,\n",
    "         '-o', label='wine consumption')\n",
    "rolling_mean = alcohol_consumption.TotalWine.rolling(window=4).mean()\n",
    "plt.plot(rolling_mean, label='trend')\n",
    "plt.legend(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rolling mean curve seems to capture the trend nicely and removes much of the seasonal movements. \n",
    "This curves allows to better appreciate the overall increase of wine consumption over time as well as the dip in consumption in 2008. \n",
    "\n",
    "To explore this rolling average further, it's nice to look at widgets. Have alook at the cell below and modify at will. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "def rolling_avg_plot(window_size):\n",
    "    plt.plot(alcohol_consumption.TotalWine, \n",
    "             '-o', label='wine consumption')\n",
    "    rolling = alcohol_consumption.TotalWine.rolling(window=window_size).mean()\n",
    "    plt.plot(rolling, label='trend')\n",
    "    plt.legend();\n",
    "    plt.show()\n",
    "\n",
    "interact(rolling_avg_plot, window_size=(0, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: plot the moving sum with a window of width 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differencing\n",
    "\n",
    "Differencing amounts to looking at the time series formed of differences between values separated by a given lag: \n",
    "\n",
    "$y'_t = y_t-y_{t-1}$\n",
    "\n",
    "for a lag of 1. Show the time series for `TotalWine` and the differenced one (with lag 1). What do you observe? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(alcohol_consumption.TotalWine, '-o', \n",
    "         label=\"original ts\")\n",
    "plt.plot(alcohol_consumption.TotalWine.diff(1), '-o', \n",
    "         label=\"differenced ts (lag=1)\")\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a feel for what a good lag should be (though here, intuitively, you should realise that a lag of `4` is a good idea), you can look at the cell below that shows differenced series for increasing lags. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def differencing_plot(d):\n",
    "    differenced_ts = alcohol_consumption.TotalWine.diff(d)\n",
    "    plt.plot(differenced_ts, '-o')\n",
    "    plt.show()\n",
    "\n",
    "interact(differencing_plot, d=(1, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autocorrelation\n",
    "\n",
    "Autocorrelation measures the correlation (similarity) between the time series and a lagged version of itself. \n",
    "\n",
    "* Use the `autocorr` method ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.autocorr.html)) to compute the autocorrelation for lag from 1 to 13\n",
    "* Display the values of the autocorrelation using a stem plot (`plt.stem`) \n",
    "\n",
    "What do you observe? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = range(1, 13)\n",
    "autocorrs = [alcohol_consumption.TotalWine.autocorr(lag=lag) \n",
    "                   for lag in lags]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.stem(lags, autocorrs)\n",
    "plt.xlabel(\"Lag\", fontsize=12)\n",
    "plt.ylabel(\"Autocorrelation\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite clear from this plot that the time series is self-similar to itself with a lag of 4 and consistently so (so also with a lag of 8, 12, etc)"
   ]
  }
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